The sensors aboard present day and proposed remote sensing spacecraft can produce prodigious amounts of information which have to be transmitted on a limited bandwidth cross-link or down-link channel. For example, the Landsat Multi-Spectral Scanner (MSS) system produces 4 bands of data at a data rate of about 28 million pixels per scene. The data produced are transmitted at a rate of 15 Mbit/s using S-band transmission. The Thematic Mapper (TM), on the other hand, produces 7 bands of information at a data rate of about 231 million pixels per scene, which TM data are transmitted at 85 Mbit/s via X-band transmission. The Earth Observing System (EOS), which is scheduled to begin operation in the late 1990s, will carry a High Resolution Imaging Spectrometer (HIRIS) designed to acquire images in 192 spectral bands. The HIRIS system is expected to produce a maximum output data rate of 300 Mbit/s.
It will be appreciated that not only do the high data rates pose a formidable communications problem, but they also impose a severe strain on the ground data storage and manipulation facilities. It will also be noted that data compression of some sort would alleviate these problems in present and future remote sensing systems.
Methods for manipulating image data have long been known. Such methods generally fall into two categories, methods for spatial image transformation and methods for spatial image compression. Whether image translation or image compression is being performed, mathematical transforms, e.g., affine transforms, have been employed. It will be appreciated that the classic affine transform is used to map pixels in one location onto another location. For example, U.S. Pat. No. 4,908,874discloses classic spatial manipulation using an affine transform, while U.S. Pat. No. 5,214,504 uses affine transforms for interpolation of interframe pixel locations. U.S. Pat. Nos. 4,631,750, 4,463,372, 4,468,688, 4,472,732, 5,046,108 and 5,175,808 disclose additional applications of the affine transform to spatial image transformation.
Affine transformations have also found their way into various spatial image compression methods. U.S. Pat. No. 4,941,193, for example, describes iterative affine transforms for compressing images using fractal modeling while U.S. Pat. No. 5,065,447 further describes affine transformations leading to a fractal transform technique.
Adaptive linear prediction has also been employed in speech synthesis as described in U.S. Pat. No. 4,022,974.